Composite financial performance index prediction – a neural networks approach
Abstract
Financial indicators are the most used variables in measuring the business performance of companies, signaling about the financial position, comprehensive income, and other significant reporting aspects. In a competitive environment, the performance measurement model allows performing comparative analysis in the same industry and between industries. This paper aims to design a composite financial index to determine the financial performance of listed companies, further used in predicting business performance through neural networks. Principal components analysis was used to build a composite financial index, employing four traditional accounting indicators and four value-based indicators for the period 2011–2018. Five experiments were conducted to predict business performance through the composite financial index. The results showed that observations from two years, of the first three experiments, indicate a better predictive behavior than the same experiments using observations from one year. Therefore, we concluded that observations from more than one year are necessary to predict the value of the financial performance index. Findings led us to the conclusion that recurrent neural networks model predicted better financial performance composite index when taken into consideration more real data for the financial performance index (2012–2018) instead of just for one year (2018).
Keyword : business performance, financial indicators, composite index, PCA, predictive behaviour, neural networks
How to Cite
Sabău Popa, D. C., Popa, D. N., Bogdan, V., & Simut, R. (2021). Composite financial performance index prediction – a neural networks approach. Journal of Business Economics and Management, 22(2), 277-296. https://doi.org/10.3846/jbem.2021.14000
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Cabinova, V., Onuferova E., Gallo, P. Jr., Gallo, P., & Gallo, J. (2018). A comparative analysis of modern performance methods in economic practice. Montenegrin Journal of Economics, 14(4), 085–096. https://doi.org/10.14254/1800-5845/2018.14-4.6
Camska, D., & Klecka, J. (2020). Comparison of prediction models applied in economic recession and expansion. Journal of Risk and Financial Management, 13(52), 1–16. https://doi.org/10.3390/jrfm13030052
Cattell, R. B. (1966). The scree plot test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276.
Elsadek, M., Elshakour, H. A., & Elyamany, A. (2017). Developing a neural networks model for evaluating financial performance of residential companies based on FCM. Journal of Mechanical and Civil Engineering (IOSR – JMCE), 44(2), 46–59. https://doi.org/10.9790/1684-1402024659
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Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45–61. https://doi.org/10.1162/003465398557320
Fields, T. D., Lys, T. Z., & Vincent, L. (2001). Empirical research on accounting choice. Journal of Accounting and Economics, 31(1–3), 255–307. https://doi.org/10.1016/S0165-4101(01)00028-3
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Gujarati, D. N. (2002). Basic econometrics (4th ed.). McGraw-Hill Higher Education.
Han, R. J., & Cao, Q. L. (2017). Fuzzy chance-constrained least squares twin support vector machine for uncertain classification. Journal of Intelligent & Fuzzy Systems, 33(5), 3041–3049. https://doi.org/10.3233/JIFS-169355
Hewamalage, H., Bergmeir, C., & Bandara, K. (2019). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388–427. https://doi.org/10.1016/j.ijforecast.2020.06.008
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Horváthová, J., Mokrišová, M., Suhányiová, A., & Suhányi, L. (2015). Selection of key performance indicators of chosen industry and their application in the formation of creditworthy model. Proceedia Economics and Finance, 34, 360–367. https://doi.org/10.1016/S2212-5671(15)01641-X
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(7), 498–520. https://doi.org/10.1037/h0070888
Hsu, S., Hsieh, J., Chih, T., & Hsu, K. (2009). A two-stage architecture for stock price forecasting by integrating the self-organizing map and support vector regression. Expert Systems with Applications, 36(4), 7947–7951. https://doi.org/10.1016/j.eswa.2008.10.065
Hu, C., Wu, Q., Li, H., Jian, S., Li, N., & Lou, Z. (2018). Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water, 10(11), 1543, 1–16. https://doi.org/10.3390/w10111543
Hu, M. Y., Jiang, C. X., & Patuwo, E. (1999). A cross-validation analysis of neural network out-ofsample performance in exchange rate forecasting. Decision Sciences, 30(1), 197–216. https://doi.org/10.1111/j.1540-5915.1999.tb01606.x
Huang, S. M., Tsai, C. F, Yen, D. C., & Cheng, Y. L. (2008). A hybrid financial analysis model for business failure prediction. Expert Systems with Applications, 35(3), 1034–1040. https://doi.org/10.1016/j.eswa.2007.08.040
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IHS Global Inc. (2017). EViews 9. Student/Lite Version [Computer software for windows]. http://www.eviews.com/download/student9/EViews%209%20Student%20Version.pdf
Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). Springer-Verlag Press.
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202
Kadhim, S. N., & Erzaij, K. R. (2020). A neural network model for financial performance prediction: The case for road works in Bahrain. Test Engineering & Management, 82, 1589–1599.
Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187–200. https://doi.org/10.1007/BF02289233
Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151. https://doi.org/10.1177/001316446002000116
Kiselakova, D., Šofranková, B., Čabinová, V., & Šoltésová, J. (2018). Analysis of enterprise performance and competitiveness to streamline managerial decisions. Polish Journal of Management Studies, 17(2), 101–111. https://doi.org/10.17512/pjms.2018.17.2.09
Kloptchenko, A., Eklund, T., Back, B., Karlsson, J., Vanharanta, H., & Visa, A. (2004). Combining data and text mining techniques for analyzing financial reports. Intelligent Systems in Accounting, Finance, and Management, 12(1), 29–41. https://doi.org/10.1002/isaf.239
Lam, M. (2004). Neural network techniques for financial performance prediction: Integrating fundamental and technical analysis. Decision Support Systems, 37(4), 567–581. https://doi.org/10.1016/S0167-9236(03)00088-5
Lee, J., Jang, D. S., & Park, S. (2017). Deep learning-based corporate performance prediction model considering technical capability. Sustainability, 9(6), 899. https://doi.org/10.3390/su9060899
Lehavy, R., Li, F., & Merkley, K. (2011). The effect of annual report readability on analyst following and the properties of their earnings forecasts. The Accounting Review, 86(3), 1087–1115. https://doi.org/10.2308/accr.00000043
Li, F. (2010). The information content of forward-looking statements in corporate filings – A Naïve Bayesian machine learning approach. Journal of Accounting Research, 48(5), 1049–1102. https://doi.org/10.1111/j.1475-679X.2010.00382.x
Li, F. Y., Jiang, Q. J., & Ke, F. (2017, September). Discussion on financial fraud of agricultural listed companies. In The 2nd International Conference on Humanities Science, Management and Education Technology (HSMET) (pp. 296–299). Zhuhai, China. https://doi.org/10.12783/dtssehs/hsmet2017/16503
Li, H., & Sun, J. (2013). Predicting business failure using an RSF-based case-based reasoning ensemble forecasting method. Journal of Forecasting, 32(2), 180–192. https://doi.org/10.1002/for.1265
Li, H., Sun, J., & Wu, J. (2010). Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods. Expert Systems with Application, 37(8), 5895–5904. https://doi.org/10.1016/j.eswa.2010.02.016
Lin, F., Liang, D., & Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38(12), 15094–15102. https://doi.org/10.1016/j.eswa.2011.05.035
Maestri, C. O. N. M., Tavares, V. B., Penedo, A. S. T., Pereira, V. S., & Coelho, R. R. A. (2019). Does the level of corporate governance predict the financial performance of the company? Evidence from the Brazilian market through artificial neural networks. Revista Containese da Ciencia Contabil, 18, 1–14. https://doi.org/10.16930/2237-766220192796
Magnusson, C., Arppe, A., Eklund, T., Barbro, B., Vanharanta, H., & Visa, A. (2005). The language of quarterly reports as an indicator of change in the company’s financial status. Information & Management, 42(4), 561–574. https://doi.org/10.1016/j.im.2004.02.008
Marginean, A., Groza, A., Nicoara, S. D., Muntean, G., Slavescu, R., & Letia, I. A. (2019, September 5–7). Towards balancing the complexity of convolutional neural network with the role of optical coherence tomography in retinal conditions. In 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP) (pp. 475–482). Cluj Napoca, Romania. https://doi.org/10.1109/ICCP48234.2019.8959714
Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603–614. https://doi.org/10.1016/j.eswa.2004.12.008
Mulford, C. W., & Comiskey, E. E. (2002). The financial numbers game, detecting creative accounting practices. John Wiley & Sons Inc.
OECD. (2008). Handbook on constructing composite indicators: Methodology and user guide.
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine., 2(11), 559–572.
Qiu, X. Y., Srinivasan, P., & Hu, Y. (2014). Supervised learning models to predict firm performance with annual reports: An empirical study. Journal of the American Society for Information Science and Technology, 65(2), 400–413. https://doi.org/10.1002/asi.22983
Ribeiro, B., & Lopes, N. (2011). Deep belief networks for financial prediction. In B. L. Lu, L. Zhang & J. Kwok (Eds.), Lecture Notes in Computer Science: Vol. 7064. Neural information processing. ICONIP 2011 (pp 766–773). Springer. https://doi.org/10.1007/978-3-642-24965-5_86
Saporta, G., & Stefanescu, M. V. (1996). Analiza datelor şi informatică. Economica Press.
Shen, F., Chao, J., & Zhao, J. (2015). Forecasting exchange rate using deep belief networks and conjugate gradient method. Neurocomputing, 167, 243–253. https://doi.org/10.1016/j.neucom.2015.04.071
Situm, M. (2013). Business failure prediction models based on expert knowledge. Czech Journal of Social Sciences, Business, and Economics, 2(4), 28–45. http://www.cjssbe.cz/journal-archive/
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